Data scientists have plenty of tools to find pattern matching in time series. But these tools can be difficult to work with. A team of researchers from the Nova University of Lisbon and Fraunhofer AICOS have come up with SSTS (Search In Time Series), an innovative tool for exploratory data analysis that use regular expression queries to search for desired patterns in a symbolic representation of timeseries data.
Duarte Folgado sheds some light on SSTS in this report from Science Trends:
The Syntactic tool for pattern Search in Time Series (SSTS) was built over the previous observations and is capable of exploring time series data using a set of 3 symbolic steps: Pre-Processing, Symbolic Connotation and Search. The SSTS tool follows what is typically made for time series analysis: (1) the time series is transformed so that only the needed information is visible, therefore being prepared for the next procedures (Pre-Processing); (2) the major features of the time series are accessed to retrieve the needed information for the pattern match (Symbolic Connotation); (3) the search is performed based on the previous step (Search).
However, in this case, the time series is transformed into a symbolic representation, being the search made with regular expression queries. By adopting a set of symbolic methods, this approach has the purpose of increasing the expressiveness in solving standard pattern and query tasks, enabling the creation of queries more closely related to the reasoning and visual analysis of the time series.
More specifically, the pre-processing stage consists of the application of routine pre-processing tasks aiming to filter and remove noise from time series. Thereafter, the time series is converted from the numerical into the symbolic domain using a connotation process which generates a sequence of symbols following a grammar formalism. This process of translation profits of our capability in observing which properties are more important.
Once the time series has been converted from numerical values to a string, queries can be performed using string occurrence mechanisms. A powerful tool commonly used for such tasks are regular expressions. The search procedure returns the intervals at which positive matches occurred.
